Identifying an accurate transcription from probabilistic inputs

ABSTRACT

An approach is provided for identifying an accurate transcription of a sentence. Options for transcriptions of each word in the sentence are determined. Probabilistic scores of the options are determined. Variations of a transcription of the sentence are generated by randomly selecting from the options with the probabilistic scores weighting the selections. Plausibility scores for the variations are generated by performing syntactic, semantic, and redundancy analyses of the variations. Based on the plausibility scores, the probabilistic scores, and the variations, tentative transcriptions of the sentence are determined and refined repeatedly by employing a genetic evolution technique until a final refined tentative transcription is the accurate transcription of the sentence.

BACKGROUND

The present invention relates to speech and text recognition, and moreparticularly to identifying an accurate transcription of written text orspeech.

Automatic systems for information extraction, decision making, theoremproving, and query answering usually perform complex calculationsinvolving large knowledge bases and a corpus of deduction rules. Thequality of the answer provided by an expert system is stronglycorrelated to the accuracy of the inputs. In many cases, the inputs areuncertain because they are the output of probabilistic systems, such asvoice recognition, or statistical methods providing hypotheses. Assumingthe most probable value for each input as the “correct” one is notoptimal because alternate input combinations which could have generateda better result are discarded.

Because probabilistic inputs may generate a large number of inputcombinations, processing every one of the combinations is not feasiblefor computational complexity reasons. Limiting the number ofalternatives for each input increases the probability of missing goodanswers, while still permitting the possibility of a large searchingspace that makes finding a solution infeasible. As one example, for asystem that has only 50 inputs, limiting each input to only twoalternatives generates a significant number (i.e., 2⁵⁰) of scenarios.Known techniques that use semantic analysis to assign scores to words orword sequences must limit the number of inputs to low numbers, such asthree to six, in order to keep the number of combinations of inputs at amanageable level.

SUMMARY

In one embodiment, the present invention provides a method of method ofidentifying an accurate transcription of a sentence. The method includesa computer determining multiple options for transcriptions of each wordincluded in the words in the sentence initially received as written textor speech. The method further includes the computer determiningprobabilistic scores of the options. The probabilistic scores indicaterespective likelihoods that the multiple options are accuratetranscriptions of each word. The method further includes the computergenerating variations of a transcription of the sentence by selectingfrom among the multiple options for the transcriptions of each word byusing numbers generated by a hardware random number generator or apseudorandom number generator. Selecting from among the options isweighted by the probabilistic scores. The method further includes thecomputer generating plausibility scores for the variations by performingsyntactic, semantic, and redundancy analyses of the variations. Theplausibility scores indicate respective likelihoods that the variationsare plausible sentences. The method further includes based on theplausibility scores, the probabilistic scores, and the variations, thecomputer determining and refining tentative transcriptions of thesentence repeatedly until a final refined tentative transcription is theaccurate transcription of the sentence by employing a genetic evolutiontechnique on the variations.

The aforementioned embodiment provides a genetic evolution techniquethat efficiently explores a wide search space which has a significantnumber of inputs and a significant number of possible values for eachinput, thereby providing a large accuracy and speed gain against knowntechniques that use brute force search or brutal truncation of theexploration of the solution space. The aforementioned embodimentefficiently finds an accurate transcription of one or more sentences,without using hard trimming of input combinations that increase theprobability of missing potentially accurate transcriptions. Theaforementioned embodiment allows for a significant number of hypothesesfor each word in a sentence and is open to a wide variety ofinterpretations of the text.

In one optional aspect of the aforementioned embodiment, the step ofdetermining and refining the tentative transcriptions includes based onthe plausibility scores, the computer dividing the variations intomutually exclusive first and second sets of the variations. The firstset indicates sentences that are more plausible than any sentenceindicated by the second set. The step of determining and refining thetentative transcriptions further includes the computer discarding thesecond set. The step of determining and refining the tentativetranscriptions further includes the computer generating couples of firstand second parent variations from the variations in the first set. Thecouples are generated by using numbers generated by the hardware randomnumber generator or the pseudorandom number generator. The step ofdetermining and refining the tentative transcriptions further includesthe computer generating child variations by generating two childvariations from each of the couples, where a word in each childvariation is inherited from the first parent variation, inherited fromthe second parent variation, or is randomly selected from the multipleoptions for the transcription of the word based on the probabilisticscores by using the hardware random number generator or the pseudorandomnumber generator. The step of determining and refining the tentativetranscriptions further includes the computer determining plausibilityscores for the child variations. The step of determining and refiningthe tentative transcriptions further includes the computer adding thechild variations to the first set of variations to create a new set ofvariations. The step of determining and refining the tentativetranscriptions further includes the computer identifying a variation inthe new set of variations as the variation having the greatestplausibility score. The step of determining and refining the tentativetranscriptions further includes based on the identified variation havingthe greatest plausibility score, the computer determining the identifiedvariation is a tentative transcription of the sentence. Theaforementioned aspect provides a genetic exploration approach thatefficiently searches a solution space while advantageously avoiding thecomputational complexity issues of known exhaustive search techniquesthat experience an exponential explosion of input combinations as thenumber of hypotheses for each input increases. The efficient search ofthe solution space advantageously avoids a need for a significant numberof servers or other significant computing resources to find the accuratetranscription; instead, the efficient search may be performed by usingmore limited computing resources such as a smartphone.

In one optional aspect of the aforementioned embodiment, the methodfurther includes the computer determining the tentative transcription isnot the accurate transcription of the sentence. The method furtherincludes based on the tentative transcription not being the accuratetranscription of the sentence, the computer refining the tentativetranscription of the sentence by repeating the steps of dividing thevariations, discarding the second set, generating the couples,generating the child variations, determining the plausibility scores forthe child variations, adding the child variations to the first set,identifying the variation, and determining the identified variation isthe tentative transcription of the sentence. The method further includesin response to the step of repeating being performed a predeterminednumber of times or a refined tentative transcription of the sentence isnot an improvement over a previously refined tentative transcription byan amount that exceeds a predetermined threshold, the computerdetermining the refined tentative transcription of the sentence is thefinal refined tentative transcription. The method further includes thecomputer presenting the final refined tentative transcription as theaccurate transcription of the sentence. The aforementioned aspectadvantageously allows for an efficient search of the solution spacebecause the repetitions (i.e., number of generations) in the geneticexploration approach may be kept to a number that is low enough thatwhen multiplied by the number of words in the sentence is significantlyless than the number of words raised to the power the number ofhypotheses for each word, which is the greater computational complexityaddressed by known exhaustive search techniques.

In one optional aspect of the aforementioned embodiment, the step ofperforming the syntactic, semantic, and redundancy analyses of thevariations includes the computer generating first scores indicatingmeasures of the syntaxes of the variations satisfying grammar rules. Thestep of performing the syntactic, semantic, and redundancy analyses ofthe variations further includes the computer generating second scoresindicating frequencies of fragments of the variations being matched tofragments included in a corpus of documents. The step of performing thesyntactic, semantic, and redundancy analyses of the variations furtherincludes the computer generating third scores based on ratios of numbersof different words in sentences indicated by the variations and totalnumbers of words in the sentences indicated by the variations. The stepof generating the plausibility scores for the variations includesgenerating each plausibility score by adding scores included in thefirst, second and third scores. The aforementioned aspect advantageouslyallows for scores to be calculated for an entire sentence.

In one optional aspect of the aforementioned embodiment, the step ofperforming the syntactic, semantic, and redundancy analyses of thevariations includes the computer performing a redundancy analysis of afirst variation included in the variations. Performing the redundancyanalysis of the first variation includes determining a number d ofdifferent words in a sentence indicated by the first variation.Performing the redundancy analysis of the first variation furtherincludes determining a total number t of words in the sentence indicatedby the first variation. Performing the redundancy analysis of the firstvariation further includes determining a redundancy score of the firstvariation by calculating 100−100d/t. The aforementioned aspectadvantageously provides a scoring system based on entropy (i.e., ameasure of word redundancy in the sentence), which cannot be usedeffectively in known exhaustive search techniques that are limited to asmall number of words (e.g., 3 to 6 words).

In one optional aspect of the aforementioned embodiment, the methodfurther includes the computer determining a redundancy score of a secondvariation included in the variations. The method further includes thecomputer determining that the redundancy score of the first variation isgreater than the redundancy score of the second variation. The methodfurther includes based on the redundancy score of the first variationbeing greater than the redundancy score of the second variation, thecomputer determining that the first variation is more likely than thesecond variation to be the accurate transcription of the sentence. Theaforementioned aspect advantageously uses a redundancy score todistinguish between different transcriptions of the sentence in terms ofaccuracy.

In another embodiment, the present invention provides a computer programproduct for identifying an accurate transcription of a sentence. Thecomputer program product includes a computer readable storage medium.Computer readable program code is stored in the computer readablestorage medium. The computer readable storage medium is not a transitorysignal per se. The computer readable program code is executed by acentral processing unit (CPU) of a computer system to cause the computersystem to perform a method. The method includes the computer systemdetermining multiple options for transcriptions of each word included inthe words in the sentence initially received as written text or speech.The method further includes the computer system determiningprobabilistic scores of the options. The probabilistic scores indicaterespective likelihoods that the multiple options are accuratetranscriptions of each word. The method further includes the computersystem generating variations of a transcription of the sentence byselecting from among the multiple options for the transcriptions of eachword by using numbers generated by a hardware random number generator ora pseudorandom number generator. Selecting from among the options isweighted by the probabilistic scores. The method further includes thecomputer system generating plausibility scores for the variations byperforming syntactic, semantic, and redundancy analyses of thevariations. The plausibility scores indicate respective likelihoods thatthe variations are plausible sentences. The method further includesbased on the plausibility scores, the probabilistic scores, and thevariations, the computer system determining and refining tentativetranscriptions of the sentence repeatedly until a final refinedtentative transcription is the accurate transcription of the sentence byemploying a genetic evolution technique on the variations.

Advantages of the aforementioned computer program product embodimentinclude the advantages discussed above relative to the embodiment thatprovides the method of identifying an accurate transcription of asentence. Optional aspects of the aforementioned computer programproduct embodiment include the aspects discussed above relative to theembodiment that provides the method of identifying an accuratetranscription of a sentence. Advantages of the optional aspects of thecomputer program product embodiment include the advantages discussedabove relative to the embodiment that provides the method of identifyingan accurate transcription of a sentence.

In another embodiment, the present invention provides a computer systemincluding a central processing unit (CPU); a memory coupled to the CPU;and a computer readable storage medium coupled to the CPU. The computerreadable storage medium contains instructions that are executed by theCPU via the memory to implement a method of identifying an accuratetranscription of a sentence. The method includes the computer systemdetermining multiple options for transcriptions of each word included inthe words in the sentence initially received as written text or speech.The method further includes the computer system determiningprobabilistic scores of the options. The probabilistic scores indicaterespective likelihoods that the multiple options are accuratetranscriptions of each word. The method further includes the computersystem generating variations of a transcription of the sentence byselecting from among the multiple options for the transcriptions of eachword by using numbers generated by a hardware random number generator ora pseudorandom number generator. Selecting from among the options isweighted by the probabilistic scores. The method further includes thecomputer system generating plausibility scores for the variations byperforming syntactic, semantic, and redundancy analyses of thevariations. The plausibility scores indicate respective likelihoods thatthe variations are plausible sentences. The method further includesbased on the plausibility scores, the probabilistic scores, and thevariations, the computer system determining and refining tentativetranscriptions of the sentence repeatedly until a final refinedtentative transcription is the accurate transcription of the sentence byemploying a genetic evolution technique on the variations.

Advantages of the aforementioned computer system embodiment include theadvantages discussed above relative to the embodiment that provides themethod of identifying an accurate transcription of a sentence. Optionalaspects of the aforementioned computer system embodiment include theaspects discussed above relative to the embodiment that provides themethod of identifying an accurate transcription of a sentence.Advantages of the optional aspects of the computer system embodimentinclude the advantages discussed above relative to the embodiment thatprovides the method of identifying an accurate transcription of asentence.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system for identifying an accuratetranscription, in accordance with embodiments of the present invention.

FIGS. 2A-2B depict a flowchart of a process of identifying an accuratetranscription, where the process is implemented in the system of FIG. 1,in accordance with embodiments of the present invention.

FIGS. 3A-3B depict an example of identifying an accurate transcriptionof a vocal query using the process of FIGS. 2A-2B, in accordance withembodiments of the present invention.

FIG. 4 is a block diagram of a computer included in the system of FIG. 1and that implements the process of FIGS. 2A-2B, in accordance withembodiments of the present invention.

DETAILED DESCRIPTION

Overview

Embodiments of the present invention iteratively progress to an accuratetranscription of an entire sentence, multiple sentences, or a paragraphby generating random variations of a transcription based onprobabilistic scores of options for each word in the sentence(s) orparagraph. Embodiments of the present invention employ a geneticalgorithm (also known as a genetic evolution technique or a geneticexploration approach) to genetically evolve the variations by matingrandom couples of parent variations and including random mutation eventsbased on the probabilistic scores of the options for each word.

Known techniques for recognizing words are limited by slow andinefficient processing of significantly large numbers of combinations.The known word recognition techniques use “goodness” metrics thatevaluate the accuracy of recognizing a word or small numbers of words,but these conventional metrics are not specifically designed to addresslong sentences or paragraphs. The aforementioned limitations of slownessand inefficiency of the known techniques provide a unique challenge thatis overcome by embodiments of the present invention, which employ agenetic evolution approach to evolve initial transcription variationscreated randomly according to probabilistic scores of each word option,thereby avoiding the processing of large numbers of combinations.Furthermore, embodiments of the present invention improve upon theconventional goodness metrics to address long sentences and paragraphsby using (1) a semantic analysis that may search a corpus of documentsfor text that matches an entire sentence of any length or multiplesentences, rather than a single word or a small number of words, and (2)a redundancy analysis that evaluates a variation of a transcription ofone or more sentences that repeats words as being more accurate thananother variation that does not repeat words.

System for Identifying an Accurate Transcription

FIG. 1 is a block diagram of a system 100 for identifying an accuratetranscription, in accordance with embodiments of the present invention.System 100 includes a computer 102 which executes a software-basedaccurate transcription identification system 104. Computer 102 receivesinput sentence(s) 106 as written text or as spoken speech. Sentence(s)106 may be a single sentence, multiple sentences, sentences thatcomprise one or more paragraphs, or sentences that comprise an entirepage of a document.

In one embodiment, an optical character recognition (OCR) system (notshown) applies OCR to input sentence(s) 106 which are written text to(i) identify possible word options 108 for the words to be transcribedfrom input sentence(s) 106 and (ii) determine probability scores 110 ofthe possible word options 108. The OCR system may be executed bycomputer 102 or by another computer (not shown). In another embodiment,a speech to text system (not shown) applies speech to text analysis toinput sentence(s) 106 which are recorded speech to (i) identify possibleword options 108 for the words to be transcribed from input sentence(s)106 and (ii) determine probability scores 110 of the possible wordoptions 108. The speech to text system may be executed by computer 102or by another computer (not shown).

Accurate transcription identification system 104 randomly selects fromamong possible word options 108 for each word in input sentence(s) 106to generate transcription variations 112 (i.e., variations of atranscription of input sentence(s) 106). The random selections areweighted based on probability scores 110. For example, for an inputsentence “What is the capital of Greece,” accurate transcriptionidentification system 104 may determine a first variation, which is “hothis the captain of green,” a second variation, which is “what ease thecapable off grease,” a third variation, which is “what is the capable ofgrease,” etc.

Accurate transcription identification system 104 determines plausibilityscores 114 of transcription variations 112. In one or more embodiments,accurate transcription identification system 104 determines plausibilityscores 114 by employing syntactic analysis, semantic analysis, andredundancy analysis of text in each variation included in transcriptionvariations 112.

Accurate transcription identification system 104 applies a geneticevolution technique to iteratively generate tentative transcriptions ofinput sentence(s) 106 that improve over the iterations until accuratetranscription identification system 104 determines that the most recenttentative transcription (i.e., final tentative transcription) does notneed to be refined further and designates the most recent tentativetranscription as accurate transcription of input 116 (i.e., an accuratetranscription of input sentence(s) 106).

The functionality of the components shown in FIG. 1 is described in moredetail in the discussion of FIGS. 2A-2B, FIGS. 3A-3B, and FIG. 4presented below.

Process for Identifying an Accurate Transcription

FIGS. 2A-2B depict a flowchart of a process of identifying an accuratetranscription, where the process is implemented in the system of FIG. 1,in accordance with embodiments of the present invention. The process ofFIGS. 2A-2B starts at step 200 in FIG. 2A. In step 202, accuratetranscription identification system 104 (see FIG. 1) receives inputsentence(s) 106 (see FIG. 1). In one embodiment, the input sentence(s)106 (see FIG. 1) include 100 or more words.

In step 204, accurate transcription identification system 104 (seeFIG. 1) determines possible word options 108 (see FIG. 1) for each ofthe words to be transcribed from input sentence(s) 106 (see FIG. 1) anddetermines probabilistic scores 110 (see FIG. 1) of the possible wordoptions 108 (see FIG. 1). A probabilistic score included inprobabilistic scores 110 (see FIG. 1) ranks a corresponding word optionincluded in possible word options 108 (see FIG. 1) in proportion to theprobability that the corresponding word option is the accuratetranscription of the word included in input sentence(s) 106 (see FIG.1). Alternatively, if probability estimates of possible word options 108are not available, the word options are considered to be equiprobable.

As an example, step 202 includes accurate transcription identificationsystem 104 (see FIG. 1) receiving a query expressed in natural languagerecorded in an audio file in an audio format. Computer 102 subjects theaudio file to a speech to text conversion algorithm, which segments theaudio file to isolate words, interprets the segmented audio file toidentify words, and associates each identified word to its meaning. Inthe segmenting, interpreting and associating steps described above, theoutput is uncertain and many options are possible, thereby determiningmany possible variations of the query. Therefore, accurate transcriptionidentification system 104 (see FIG. 1) lists many hypotheses for eachword and determines a confidence score (i.e., a probabilistic score) foreach option. Accurate transcription identification system 104 (seeFIG. 1) determines that using a brute force, exhaustive search techniqueto identify a meaningful, accurate query from among all the possiblevariations is infeasible, and therefore continues with the steps ofFIGS. 2A-2B described below.

In step 206, accurate transcription identification system 104 (seeFIG. 1) generates N transcription variations of the input sentence(s) byrandomly selecting from among possible word options 108 (see FIG. 1) foreach word to be transcribed from input sentence(s) 106 (see FIG. 1). Therandom selections are weighted based on probabilistic scores 110 (seeFIG. 1) of the possible word options 108 (see FIG. 1). As used herein inthe discussion of FIGS. 2A-2B, N is an integer greater than or equal tofour. N must be at least four because N/4 couples are generated in step214, as discussed below. Accurate transcription identification system104 (see FIG. 1) performs the random selections by using numbersgenerated by a hardware random number generator or a software-basedpseudorandom number generator.

In step 208, accurate transcription identification system 104 (seeFIG. 1) generates N plausibility scores for the N transcriptionvariations, respectively, where the N transcription variations aregenerated in step 206. Accurate transcription identification system 104(see FIG. 1) ranks the N transcription variations according to thecorresponding N plausibility scores. In one embodiment, a firsttranscription variation having a higher plausibility score than a secondtranscription variation means that the first transcription variation ismore meaningful, more accurate, more reasonable, and is better formedthan the second transcription variation.

In one embodiment, accurate transcription identification system 104 (seeFIG. 1) performs step 208 by performing syntactic, semantic, andredundancy analyses to generate respective syntactic, semantic, andredundancy scores of the N transcription variations. For each of the Ntranscription variations, accurate transcription identification system104 (see FIG. 1) combines the corresponding syntactic, semantic, andredundancy scores to generate a plausibility score for the transcriptionvariation. In one embodiment, the plausibility score for a transcriptionvariation is the sum of the syntactic, semantic, and redundancy scores.By using a combination of the syntactic, semantic, and redundancyscores, a higher plausibility score indicates a transcription variationthat is more syntactically sound, has a meaning that is more reasonableby the evidence of similar text in the corpus of documents, and has atendency to repeat the same words. For example, if an input has nineinstances of the word “Greece,” but the transcription variations areselecting between “Greece” and “grease” for the nine instances, then thesolution generated by accurate transcription identification system 104(see FIG. 1) favors a solution that includes “Greece” being repeatednine times, which increases the redundancy score.

Prior to step 208, another system (not shown in FIG. 1) performs alexical analysis of the words in input sentence(s) 106 (see FIG. 1) toensure that the input sentence(s) received in step 202 are in a propernatural language by being matched with entries in a dictionary.

The aforementioned syntactic analysis includes accurate transcriptionidentification system 104 (see FIG. 1) checking the transcriptionvariations against grammar rules to measure how well the variationsproperly follow grammar rules and include an evaluation of the usage ofsubject, verb and object in a sentence, as well as verb conjugation,singular and plural concordance, etc. In response to the syntacticanalysis, accurate transcription identification system 104 (see FIG. 1)outputs a syntactic score in a predetermined range of numbers (e.g., theintegers 0 to 100, where 0 identifies an entirely improper grammar and100 identifies a perfectly correct grammar).

The aforementioned semantic analysis includes accurate transcriptionidentification system 104 (see FIG. 1) performing a search of thetranscription variations in a corpus of documents. In one embodiment,accurate transcription identification system 104 (see FIG. 1) performsthe searches at a sentence-level (i.e., each fragment being searched isa full sentence). Accurate transcription identification system 104 (seeFIG. 1) maps the result of the search for each fragment to a score in apredetermined range (e.g., 0 to 100) and then calculates an average ofthe scores to generate an overall semantic score for the entiretranscription variation.

The aforementioned redundancy analysis includes accurate transcriptionidentification system 104 (see FIG. 1) determining the number of wordsin a transcription variation and the number of different words in thetranscription variation and determining a redundancy score based on theaforementioned number of words and number of different words. In oneembodiment, accurate transcription identification system 104 (seeFIG. 1) calculates the redundancy score as:

$100 - {100*\frac{{number}\mspace{14mu}{of}\mspace{14mu}{different}\mspace{14mu}{words}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{variation}}{{total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{words}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{20mu}{variation}}}$

In step 210, based on the plausibility scores generated in step 208,accurate transcription identification system 104 (see FIG. 1) identifiesthe best N/2 transcription variations included in the N transcriptionvariations and the worst N/2 transcription variations included in the Ntranscription variations. The best N/2 transcription variations are thetranscription variations included in the N transcription variations thathave the greatest N/2 plausibility scores among the N plausibilityscores generated in step 208. The worst N/2 transcription variations arethe transcription variations included in the N transcription variationsthat have the lowest N/2 plausibility scores among the N plausibilityscores generated in step 208. Step 210 is the start of a loop used in agenetic evolution technique applied to the transcription variations todetermine an accurate transcription of input sentence(s) 106 (see FIG.1).

Step 210 also begins a loop in the process of FIGS. 2A-2B thatimplements a genetic evolution technique that identifies an accuratetranscription variation from among a set of transcription variations.

In step 212, accurate transcription identification system 104 (seeFIG. 1) discards the worst N/2 transcription variations identified instep 210 so that the discarded transcription variations may be replacedwith new variations obtained through genetic evolution of the best N/2transcription variations, as described in the remaining steps of theprocess of FIGS. 2A-2B.

In step 214, accurate transcription identification system 104 (seeFIG. 1) generates N/4 parent couples of transcription variations byrandomly coupling the transcription variations included in the best N/2variations identified in step 210. Accurate transcription identificationsystem 104 (see FIG. 1) performs the random coupling by using numbersgenerated by a hardware random number generator or a software-basedpseudorandom number generator.

Following step 214, the process of FIGS. 2A-2B continues with step 216in FIG. 2B. In step 216, accurate transcription identification system104 (see FIG. 1) generates N/2 children transcription variations bygenerating two children transcription variations from each of the N/4parent couples generated in step 214 (see FIG. 2A). For a given word ina child transcription variation generated from a parent coupleconsisting of first and second parents, accurate transcriptionidentification system 104 (see FIG. 1) generates the word by (i)inheriting (i.e., selecting) the word from the first parent based on afirst predetermined probability, (ii) inheriting the word from thesecond parent based on a second predetermined probability, or (iii)randomly selecting from the possible options for the word. Generatingthe word by randomly selecting from among the possible options for theword is based on a third predetermined probability. The possible optionsfor the word are included in the possible word options 108 (see FIG. 1).The sum of the first, second, and third predetermined probabilities isequal to one. For example, accurate transcription identification system104 (see FIG. 1) may (i) with a probability of 45%, inherit a wordoption for a first word from a first parent, or (ii) with a probabilityof 45%, inherit a word option for the first word from a second parent,or (iii) with a probability of 10% randomly select a word option fromamong the possible word options for the first word. For the randomselection from the possible options for the word, accurate transcriptionidentification system 104 (see FIG. 1) makes the selection based on theprobabilistic scores that correspond to the possible options for theword, where the probabilistic scores corresponding to the possibleoptions for the word are included in the probabilistic scores 110 (seeFIG. 1). Furthermore, accurate transcription identification system 104(see FIG. 1) uses a number generated by a hardware random numbergenerator or a software-based pseudorandom number generator to make therandom selection from the possible options for the word.

In step 218, accurate transcription identification system 104 (seeFIG. 1) generates N/2 plausibility scores for the N/2 childrentranscription variations generated in step 216. In one embodiment,accurate transcription identification system 104 (see FIG. 1) performsstep 218 by performing the aforementioned syntactic, semantic, andredundancy analyses on the N/2 children transcription variations.

In step 220, accurate transcription identification system 104 (seeFIG. 1) adds the N/2 children transcription variations to the N/2 parenttranscription variations (i.e., the best N/2 transcription variationsidentified in the most recent performance of step 210 in FIG. 2A) tocreate a new set of N transcription variations.

In step 222, accurate transcription identification system 104 (seeFIG. 1) determines a tentative accurate transcription (i.e., a tentativesolution) as the transcription variation in the new set of Ntranscription variations that has the greatest (i.e., highest)plausibility score.

In step 224, accurate transcription identification system 104 (seeFIG. 1) determines whether the tentative solution determined in step 222needs to be refined. In one embodiment, accurate transcriptionidentification system 104 (see FIG. 1) refines the tentative solution apredetermined number of times (i.e., for a fixed number of generationsby looping through the Yes branch of step 224 a predetermined number oftimes) (e.g., performs 100 iterations of the loop starting at the Yesbranch of step 224). In another embodiment, accurate transcriptionidentification system 104 (see FIG. 1) refines the tentative solutionthrough the Yes branch of step 224 iteratively for at least apredetermined number of times and performs further iterative refinementsuntil the plausibility score of the tentative solution does not improveover the previous tentative solution by a threshold amount (i.e., thefitness of the highest ranking solution is reaching or has reached aplateau such that successive iterations do not produce better results).

If accurate transcription identification system 104 (see FIG. 1)determines in step 224 that the tentative solution determined in step222 needs to be refined, then the Yes branch of step 224 is followed andthe process of FIGS. 2A-2B loops back to step 210 (see FIG. 2A) usingthe new set of N transcription variations.

If accurate transcription identification system 104 (see FIG. 1)determines in step 224 that the tentative solution determined in step222 no longer needs to be refined, then the No branch of step 224 isfollowed and step 226 is performed.

In step 226, accurate transcription identification system 104 (seeFIG. 1) identifies and presents the tentative accurate transcription asthe final accurate transcription.

The process of FIGS. 2A-2B ends at step 228.

Example

FIGS. 3A-3B depict an example of identifying an accurate transcriptionof a vocal query using the process of FIGS. 2A-2B, in accordance withembodiments of the present invention. In step 300 in FIG. 3A, accuratetranscription identification system 104 (see FIG. 1) receives a vocalquery that consists of six words (i.e., Word 1, Word 2, . . . , Word 6).Step 300 is an example of step 202 (see FIG. 2A)

In step 302, accurate transcription identification system 104 (seeFIG. 1) determines the possible word options for Word 1, Word 2, . . . ,Word 6. For example, accurate transcription identification system 104(see FIG. 1) determines that there are five possible word options forWord 1: hot, what, worm, was, and hod. In step 302, accuratetranscription identification system 104 (see FIG. 1) also determines theprobabilistic scores of the possible word options for each of Word 1,Word 2, . . . , Word 6. In step 302, the probabilistic scores are inparentheses following the words to which the scores correspond. Forinstance, accurate transcription identification system 104 (see FIG. 1)determines that for the possible word options for Word 1, theprobabilistic scores for hot, what, work, was, and hod are 60, 40, 25,30, and 10, respectively. Step 302 is an example of step 204 (see FIG.2A).

In step 304, accurate transcription identification system 104 (seeFIG. 1) generates N transcription variations, including Variations 1, 2,and 3, which are “hot his the captain of green,” “what ease the capableoff grease,” and “worm is the capital off green,” respectively. Step 304is an example of step 206 (see FIG. 2A).

In step 306, accurate transcription identification system 104 (seeFIG. 1) determines plausibility scores for the N transcriptionvariations, ranks the N transcription variations according to theplausibility scores, and identifies the best N/2 transcriptionvariations according the ranking, where the best N/2 transcriptionvariations include Best variations 1, 2, and 3, which include “what isthe capable of grease,” “what is the Capitol of Greece,” and “what histhe capital of Greece,” respectively. Accurate transcriptionidentification system 104 (see FIG. 1) determines the plausibilityscores by performing the syntactic, semantic, and redundancy analyses onthe N transcription variations generated in step 304. Step 306 is anexample of steps 208 and 210 in FIG. 2A.

In step 308 in FIG. 3B, accurate transcription identification system 104(see FIG. 1) randomly couples the variations included in the best N/2transcription variations (i.e., the best N/2 parent transcriptionvariations), which generates N/4 parent couples of transcriptionvariations, including Couple 1 which consists of Parent 1 and Parent 2(i.e., p1 and p2), where p1 is the transcription variation “what is thecapable of grease” and p2 is the transcription variation “what his thecapital of Greece.” Step 308 is an example of step 214 (see FIG. 2A).

In step 310, accurate transcription identification system 104 (seeFIG. 1) generates N/2 children transcription variations by generatingtwo child transcription variations from each of the N/4 parent couples,including the child transcription variation Child 1 and the childtranscription variation Child 2 from Couple 1. For a given word in achild transcription variation which was generated from a coupleconsisting of a first and second parent, the word is (i) inherited fromthe first parent according to a first probability, (ii) inherited fromthe second parent according to a second probability, or (iii) randomlyselected from the possible word options for the word based on theprobabilistic scores of the word options.

In step 310, (p1) means the corresponding word is inherited from thefirst parent, (p2) means the corresponding word is inherited from thesecond parent, and (random) means the corresponding word is randomlyselected from the possible word options for the word.

For the example in step 310, the probability of inheriting from p1 is45%, the probability of inheriting from p2 is 45% and the probability ofrandomly selecting from the possible word options is 10%. For Child 1,accurate transcription identification system 104 (see FIG. 1) inherits“what” as Word 1 from p1, inherits “is” as Word 2 from p1, randomlyselects the word option “the” as Word 3, inherits “capital” as Word 4from p2, inherits “of” as Word 5 from p1, and inherits “grease” as Word6 from p1. For Child 2, accurate transcription identification system 104(see FIG. 1) inherits “what” as Word 1 from p2, randomly selects theword option “ease” as Word 2, inherits “the” as Word 3 from p1, inherits“capital” as Word 4 from p2, inherits “of” as Word 5 from p2, andinherits “Greece” as Word 6 from p2. Step 310 is an example of step 216(see FIG. 2B).

After step 310 and prior to step 312, accurate transcriptionidentification system 104 (see FIG. 1) performs syntactic, semantic, andredundancy analyses to generate plausibility scores for the N/2 childrengenerated in step 310.

In step 312, accurate transcription identification system 104 (seeFIG. 1) creates a new set of N transcription variations by adding theN/2 children to the N/2 parent transcription variations that were usedin the random coupling in step 308. Step 312 is an example of step 220(see FIG. 2B).

In step 314, accurate transcription identification system 104 (seeFIG. 1) determines a tentative solution by determining that “what easethe capital of Greece” has the greatest plausibility score among the newset of N transcription variations. The tentative solution of “what easethe capital of Greece” will guide successive iterations of the geneticevolution technique to generate an accurate transcription variation.Step 314 is an example of step 222 (see FIG. 2B).

In step 316, accurate transcription identification system 104 (seeFIG. 1) refines the tentative solution via looping through the Yesbranch of step 224 (see FIG. 2A) to iteratively perform step 210 in FIG.2A through step 224 in FIG. 2B, which generates a tentativetranscription of “what is the capital of Greece” after performing 100iterations, which is equal to the predetermined number of 100iterations. In step 316, accurate transcription identification system104 (see FIG. 1) determines that no further refinements of the tentativetranscription are needed because the predetermined number of iterationshave been performed, and in response, identifies the most recenttentative transcription of “what is the capital of Greece” as the finalaccurate transcription of the vocal query received in step 300 (see FIG.3A). Step 316 is an example of step 224 (see FIG. 2B), iterativelyperforming the loop starting at the Yes branch of step 224 (see FIG.2B), and step 226 (see FIG. 2B).

Computer System

FIG. 4 is a block diagram of a computer 102 included in the system ofFIG. 1 and that implements the process of FIGS. 2A-2B, in accordancewith embodiments of the present invention. Computer 102 is a computersystem that generally includes a central processing unit (CPU) 402, amemory 404, an input/output (I/O) interface 406, and a bus 408. Further,computer 102 is coupled to I/O devices 410 and a computer data storageunit 412. CPU 402 performs computation and control functions of computer102, including executing instructions included in program code 414 foraccurate transcription identification system 104 (see FIG. 1) to performa method of identifying an accurate transcription, where theinstructions are executed by CPU 402 via memory 404. CPU 402 may includea single processing unit, or be distributed across one or moreprocessing units in one or more locations (e.g., on a client andserver).

Memory 404 includes a known computer readable storage medium, which isdescribed below. In one embodiment, cache memory elements of memory 404provide temporary storage of at least some program code (e.g., programcode 414) in order to reduce the number of times code must be retrievedfrom bulk storage while instructions of the program code are executed.Moreover, similar to CPU 402, memory 404 may reside at a single physicallocation, including one or more types of data storage, or be distributedacross a plurality of physical systems in various forms. Further, memory404 can include data distributed across, for example, a local areanetwork (LAN) or a wide area network (WAN).

I/O interface 406 includes any system for exchanging information to orfrom an external source. I/O devices 410 include any known type ofexternal device, including a display, keyboard, etc. Bus 408 provides acommunication link between each of the components in computer 102, andmay include any type of transmission link, including electrical,optical, wireless, etc.

I/O interface 406 also allows computer 102 to store information (e.g.,data or program instructions such as program code 414) on and retrievethe information from computer data storage unit 412 or another computerdata storage unit (not shown). Computer data storage unit 412 includes aknown computer-readable storage medium, which is described below. In oneembodiment, computer data storage unit 412 is a non-volatile datastorage device, such as a magnetic disk drive (i.e., hard disk drive) oran optical disc drive (e.g., a CD-ROM drive which receives a CD-ROMdisk).

Memory 404 and/or storage unit 412 may store computer program code 414that includes instructions that are executed by CPU 402 via memory 404to identify an accurate transcription. Although FIG. 4 depicts memory404 as including program code, the present invention contemplatesembodiments in which memory 404 does not include all of code 414simultaneously, but instead at one time includes only a portion of code414.

Further, memory 404 may include an operating system (not shown) and mayinclude other systems not shown in FIG. 4.

Storage unit 412 and/or one or more other computer data storage units(not shown) that are coupled to computer 102 may include possible wordoptions 108 (see FIG. 1), probabilistic scores 110 (see FIG. 1),transcriptions variations 112 (see FIG. 1) and/or plausibility scores114 (see FIG. 1).

As will be appreciated by one skilled in the art, in a first embodiment,the present invention may be a method; in a second embodiment, thepresent invention may be a system; and in a third embodiment, thepresent invention may be a computer program product.

Any of the components of an embodiment of the present invention can bedeployed, managed, serviced, etc. by a service provider that offers todeploy or integrate computing infrastructure with respect to identifyingan accurate transcription. Thus, an embodiment of the present inventiondiscloses a process for supporting computer infrastructure, where theprocess includes providing at least one support service for at least oneof integrating, hosting, maintaining and deploying computer-readablecode (e.g., program code 414) in a computer system (e.g., computer 102)including one or more processors (e.g., CPU 402), wherein theprocessor(s) carry out instructions contained in the code causing thecomputer system to identify an accurate transcription. Anotherembodiment discloses a process for supporting computer infrastructure,where the process includes integrating computer-readable program codeinto a computer system including a processor. The step of integratingincludes storing the program code in a computer-readable storage deviceof the computer system through use of the processor. The program code,upon being executed by the processor, implements a method of identifyingan accurate transcription.

While it is understood that program code 414 for identifying an accuratetranscription may be deployed by manually loading directly in client,server and proxy computers (not shown) via loading a computer-readablestorage medium (e.g., computer data storage unit 412), program code 414may also be automatically or semi-automatically deployed into computer102 by sending program code 414 to a central server or a group ofcentral servers. Program code 414 is then downloaded into clientcomputers (e.g., computer 102) that will execute program code 414.Alternatively, program code 414 is sent directly to the client computervia e-mail. Program code 414 is then either detached to a directory onthe client computer or loaded into a directory on the client computer bya button on the e-mail that executes a program that detaches programcode 414 into a directory. Another alternative is to send program code414 directly to a directory on the client computer hard drive. In a casein which there are proxy servers, the process selects the proxy servercode, determines on which computers to place the proxy servers' code,transmits the proxy server code, and then installs the proxy server codeon the proxy computer. Program code 414 is transmitted to the proxyserver and then it is stored on the proxy server.

Another embodiment of the invention provides a method that performs theprocess steps on a subscription, advertising and/or fee basis. That is,a service provider can offer to create, maintain, support, etc. aprocess of identifying an accurate transcription. In this case, theservice provider can create, maintain, support, etc. a computerinfrastructure that performs the process steps for one or morecustomers. In return, the service provider can receive payment from thecustomer(s) under a subscription and/or fee agreement, and/or theservice provider can receive payment from the sale of advertisingcontent to one or more third parties.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) (i.e., memory 404 and computer data storage unit 412)having computer readable program instructions 414 thereon for causing aprocessor (e.g., CPU 402) to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions (e.g., program code 414) for use by aninstruction execution device (e.g., computer 102). The computer readablestorage medium may be, for example, but is not limited to, an electronicstorage device, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium includes thefollowing: a portable computer diskette, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions (e.g., program code 414)described herein can be downloaded to respective computing/processingdevices (e.g., computer 102) from a computer readable storage medium orto an external computer or external storage device (e.g., computer datastorage unit 412) via a network (not shown), for example, the Internet,a local area network, a wide area network and/or a wireless network. Thenetwork may comprise copper transmission cables, optical transmissionfibers, wireless transmission, routers, firewalls, switches, gatewaycomputers and/or edge servers. A network adapter card (not shown) ornetwork interface (not shown) in each computing/processing devicereceives computer readable program instructions from the network andforwards the computer readable program instructions for storage in acomputer readable storage medium within the respectivecomputing/processing device.

Computer readable program instructions (e.g., program code 414) forcarrying out operations of the present invention may be assemblerinstructions, instruction-set-architecture (ISA) instructions, machineinstructions, machine dependent instructions, microcode, firmwareinstructions, state-setting data, configuration data for integratedcircuitry, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++, or the like, andprocedural programming languages, such as the “C” programming languageor similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations (e.g., FIGS. 2A-2B) and/or block diagrams (e.g.,FIG. 1 and FIG. 4) of methods, apparatus (systems), and computer programproducts according to embodiments of the invention. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions (e.g., program code 414).

These computer readable program instructions may be provided to aprocessor (e.g., CPU 402) of a general purpose computer, special purposecomputer, or other programmable data processing apparatus (e.g.,computer 102) to produce a machine, such that the instructions, whichexecute via the processor of the computer or other programmable dataprocessing apparatus, create means for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks. Thesecomputer readable program instructions may also be stored in a computerreadable storage medium (e.g., computer data storage unit 412) that candirect a computer, a programmable data processing apparatus, and/orother devices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions (e.g., program code 414) mayalso be loaded onto a computer (e.g. computer 102), other programmabledata processing apparatus, or other device to cause a series ofoperational steps to be performed on the computer, other programmableapparatus or other device to produce a computer implemented process,such that the instructions which execute on the computer, otherprogrammable apparatus, or other device implement the functions/actsspecified in the flowchart and/or block diagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While embodiments of the present invention have been described hereinfor purposes of illustration, many modifications and changes will becomeapparent to those skilled in the art. Accordingly, the appended claimsare intended to encompass all such modifications and changes as fallwithin the true spirit and scope of this invention.

What is claimed is:
 1. A method of identifying an accurate transcriptionof a sentence, the method comprising the steps of: a computerdetermining multiple options for transcriptions of each word included inthe words in the sentence initially received as written text or speech;the computer determining probabilistic scores of the options, theprobabilistic scores indicating respective likelihoods that the multipleoptions are accurate transcriptions of each word; the computergenerating variations of a transcription of the sentence by selectingfrom among the multiple options for the transcriptions of each word byusing numbers generated by a hardware random number generator or apseudorandom number generator, the selecting being weighted by theprobabilistic scores; the computer generating plausibility scores forthe variations by performing syntactic, semantic, and redundancyanalyses of the variations, the plausibility scores indicatingrespective likelihoods that the variations are plausible sentences; andbased on the plausibility scores, the probabilistic scores, and thevariations, the computer determining and refining tentativetranscriptions of the sentence repeatedly until a final refinedtentative transcription is the accurate transcription of the sentence byemploying a genetic evolution technique on the variations.
 2. The methodof claim 1, wherein the step of determining and refining the tentativetranscriptions includes the steps of: based on the plausibility scores,the computer dividing the variations into mutually exclusive first andsecond sets of the variations, the first set indicating sentences thatare more plausible than any sentence indicated by the second set; thecomputer discarding the second set; the computer generating couples offirst and second parent variations from the variations in the first set,the couples being generated by using numbers generated by the hardwarerandom number generator or the pseudorandom number generator; thecomputer generating child variations by generating two child variationsfrom each of the couples, where a word in each child variation isinherited from the first parent variation, inherited from the secondparent variation, or is randomly selected from the multiple options forthe transcription of the word based on the probabilistic scores by usingthe hardware random number generator or the pseudorandom numbergenerator; the computer determining plausibility scores for the childvariations; the computer adding the child variations to the first set ofvariations to create a new set of variations; and the computeridentifying a variation in the new set of variations as the variationhaving the greatest plausibility score and based on the identifiedvariation having the greatest plausibility score, the computerdetermining the identified variation is a tentative transcription of thesentence.
 3. The method of claim 2, further comprising the steps of: thecomputer determining the tentative transcription is not the accuratetranscription of the sentence; based on the tentative transcription notbeing the accurate transcription of the sentence, the computer refiningthe tentative transcription of the sentence by repeating the steps ofdividing the variations, discarding the second set, generating thecouples, generating the child variations, determining the plausibilityscores for the child variations, adding the child variations to thefirst set, identifying the variation, and determining the identifiedvariation is the tentative transcription of the sentence; and inresponse to the step of repeating being performed a predetermined numberof times or a refined tentative transcription of the sentence is not animprovement over a previously refined tentative transcription by anamount that exceeds a predetermined threshold, the computer determiningthe refined tentative transcription of the sentence is the final refinedtentative transcription; and the computer presenting the final refinedtentative transcription as the accurate transcription of the sentence.4. The method of claim 1, wherein the step of performing the syntactic,semantic, and redundancy analyses of the variations includes the stepsof: the computer generating first scores indicating measures of thesyntaxes of the variations satisfying grammar rules; the computergenerating second scores indicating frequencies of fragments of thevariations being matched to fragments included in a corpus of documents;and the computer generating third scores based on ratios of numbers ofdifferent words in sentences indicated by the variations and totalnumbers of words in the sentences indicated by the variations, whereinthe step of generating the plausibility scores for the variationsincludes generating each plausibility score by adding scores included inthe first, second and third scores.
 5. The method of claim 1, whereinthe step of performing the syntactic, semantic, and redundancy analysesof the variations includes the step of the computer performing aredundancy analysis of a first variation included in the variations by:determining a number d of different words in a sentence indicated by thefirst variation; determining a total number t of words in the sentenceindicated by the first variation; and determining a redundancy score ofthe first variation by calculating 100−100d/t.
 6. The method of claim 5,further comprising the steps of: the computer determining a redundancyscore of a second variation included in the variations; the computerdetermining that the redundancy score of the first variation is greaterthan the redundancy score of the second variation; and based on theredundancy score of the first variation being greater than theredundancy score of the second variation, the computer determining thatthe first variation is more likely than the second variation to be theaccurate transcription of the sentence.
 7. The method of claim 1,further comprising the steps of: the computer receiving the sentence asa query expressed in natural language in a recorded voice; the computeridentifying the words in the sentence by segmenting an audio file thatstores the recorded voice and interpreting the segmented audio file; andthe computer associating the identified words with respective meanings,wherein the multiple options for the transcriptions of each word arebased on multiple results of each of the steps of segmenting the audiofile, interpreting the segmented audio file, and associating theidentified words with the respective meanings.
 8. The method of claim 1,further comprising the step of: providing at least one support servicefor at least one of creating, integrating, hosting, maintaining, anddeploying computer readable program code in the computer, the programcode being executed by a processor of the computer to implement thesteps of determining the multiple options for the transcriptions of eachword, determining the probabilistic scores, generating the variations ofthe transcription of the sentence, generating the plausibility scoresfor the variations, and determining and refining tentativetranscriptions of the sentence repeatedly until the final refinedtentative transcription is the accurate transcription of the sentence.9. A computer program product for identifying an accurate transcriptionof a sentence, the computer program product comprising a computerreadable storage medium having computer readable program code stored onthe computer readable storage medium, wherein the computer readablestorage medium is not a transitory signal per se, the computer readableprogram code being executed by a central processing unit (CPU) of acomputer system to cause the computer system to perform a methodcomprising the steps of: the computer system determining multipleoptions for transcriptions of each word included in the words in thesentence initially received as written text or speech; the computersystem determining probabilistic scores of the options, theprobabilistic scores indicating respective likelihoods that the multipleoptions are accurate transcriptions of each word; the computer systemgenerating variations of a transcription of the sentence by selectingfrom among the multiple options for the transcriptions of each word byusing numbers generated by a hardware random number generator or apseudorandom number generator, the selecting being weighted by theprobabilistic scores; the computer system generating plausibility scoresfor the variations by performing syntactic, semantic, and redundancyanalyses of the variations, the plausibility scores indicatingrespective likelihoods that the variations are plausible sentences; andbased on the plausibility scores, the probabilistic scores, and thevariations, the computer system determining and refining tentativetranscriptions of the sentence repeatedly until a final refinedtentative transcription is the accurate transcription of the sentence byemploying a genetic evolution technique on the variations.
 10. Thecomputer program product of claim 9, wherein the step of determining andrefining the tentative transcriptions includes the steps of: based onthe plausibility scores, the computer system dividing the variationsinto mutually exclusive first and second sets of the variations, thefirst set indicating sentences that are more plausible than any sentenceindicated by the second set; the computer system discarding the secondset; the computer system generating couples of first and second parentvariations from the variations in the first set, the couples beinggenerated by using numbers generated by the hardware random numbergenerator or the pseudorandom number generator; the computer systemgenerating child variations by generating two child variations from eachof the couples, where a word in each child variation is inherited fromthe first parent variation, inherited from the second parent variation,or is randomly selected from the multiple options for the transcriptionof the word based on the probabilistic scores by using the hardwarerandom number generator or the pseudorandom number generator; thecomputer system determining plausibility scores for the childvariations; the computer system adding the child variations to the firstset of variations to create a new set of variations; and the computersystem identifying a variation in the new set of variations as thevariation having the greatest plausibility score and based on theidentified variation having the greatest plausibility score, thecomputer system determining the identified variation is a tentativetranscription of the sentence.
 11. The computer program product of claim10, wherein the method further comprises the steps of: the computersystem determining the tentative transcription is not the accuratetranscription of the sentence; based on the tentative transcription notbeing the accurate transcription of the sentence, the computer systemrefining the tentative transcription of the sentence by repeating thesteps of dividing the variations, discarding the second set, generatingthe couples, generating the child variations, determining theplausibility scores for the child variations, adding the childvariations to the first set, identifying the variation, and determiningthe identified variation is the tentative transcription of the sentence;and in response to the step of repeating being performed a predeterminednumber of times or a refined tentative transcription of the sentence isnot an improvement over a previously refined tentative transcription byan amount that exceeds a predetermined threshold, the computer systemdetermining the refined tentative transcription of the sentence is thefinal refined tentative transcription; and the computer systempresenting the final refined tentative transcription as the accuratetranscription of the sentence.
 12. The computer program product of claim9, wherein the step of performing the syntactic, semantic, andredundancy analyses of the variations includes the steps of: thecomputer system generating first scores indicating measures of thesyntaxes of the variations satisfying grammar rules; the computer systemgenerating second scores indicating frequencies of fragments of thevariations being matched to fragments included in a corpus of documents;and the computer system generating third scores based on ratios ofnumbers of different words in sentences indicated by the variations andtotal numbers of words in the sentences indicated by the variations,wherein the step of generating the plausibility scores for thevariations includes generating each plausibility score by adding scoresincluded in the first, second and third scores.
 13. The computer programproduct of claim 9, wherein the step of performing the syntactic,semantic, and redundancy analyses of the variations includes the step ofthe computer system performing a redundancy analysis of a firstvariation included in the variations by: determining a number d ofdifferent words in a sentence indicated by the first variation;determining a total number t of words in the sentence indicated by thefirst variation; and determining a redundancy score of the firstvariation by calculating 100−100d/t.
 14. The computer program product ofclaim 13, wherein the method further comprises the steps of: thecomputer system determining a redundancy score of a second variationincluded in the variations; the computer system determining that theredundancy score of the first variation is greater than the redundancyscore of the second variation; and based on the redundancy score of thefirst variation being greater than the redundancy score of the secondvariation, the computer system determining that the first variation ismore likely than the second variation to be the accurate transcriptionof the sentence.
 15. A computer system comprising: a central processingunit (CPU); a memory coupled to the CPU; and a computer readable storagemedium coupled to the CPU, the computer readable storage mediumcontaining instructions that are executed by the CPU via the memory toimplement a method of identifying an accurate transcription of asentence, the method comprising the steps of: the computer systemdetermining multiple options for transcriptions of each word included inthe words in the sentence initially received as written text or speech;the computer system determining probabilistic scores of the options, theprobabilistic scores indicating respective likelihoods that the multipleoptions are accurate transcriptions of each word; the computer systemgenerating variations of a transcription of the sentence by selectingfrom among the multiple options for the transcriptions of each word byusing numbers generated by a hardware random number generator or apseudorandom number generator, the selecting being weighted by theprobabilistic scores; the computer system generating plausibility scoresfor the variations by performing syntactic, semantic, and redundancyanalyses of the variations, the plausibility scores indicatingrespective likelihoods that the variations are plausible sentences; andbased on the plausibility scores, the probabilistic scores, and thevariations, the computer system determining and refining tentativetranscriptions of the sentence repeatedly until a final refinedtentative transcription is the accurate transcription of the sentence byemploying a genetic evolution technique on the variations.
 16. Thecomputer system of claim 15, wherein the step of determining andrefining the tentative transcriptions includes the steps of: based onthe plausibility scores, the computer system dividing the variationsinto mutually exclusive first and second sets of the variations, thefirst set indicating sentences that are more plausible than any sentenceindicated by the second set; the computer system discarding the secondset; the computer system generating couples of first and second parentvariations from the variations in the first set, the couples beinggenerated by using numbers generated by the hardware random numbergenerator or the pseudorandom number generator; the computer systemgenerating child variations by generating two child variations from eachof the couples, where a word in each child variation is inherited fromthe first parent variation, inherited from the second parent variation,or is randomly selected from the multiple options for the transcriptionof the word based on the probabilistic scores by using the hardwarerandom number generator or the pseudorandom number generator; thecomputer system determining plausibility scores for the childvariations; the computer system adding the child variations to the firstset of variations to create a new set of variations; and the computersystem identifying a variation in the new set of variations as thevariation having the greatest plausibility score and based on theidentified variation having the greatest plausibility score, thecomputer system determining the identified variation is a tentativetranscription of the sentence.
 17. The computer system of claim 16,wherein the method further comprises the steps of: the computer systemdetermining the tentative transcription is not the accuratetranscription of the sentence; based on the tentative transcription notbeing the accurate transcription of the sentence, the computer systemrefining the tentative transcription of the sentence by repeating thesteps of dividing the variations, discarding the second set, generatingthe couples, generating the child variations, determining theplausibility scores for the child variations, adding the childvariations to the first set, identifying the variation, and determiningthe identified variation is the tentative transcription of the sentence;and in response to the step of repeating being performed a predeterminednumber of times or a refined tentative transcription of the sentence isnot an improvement over a previously refined tentative transcription byan amount that exceeds a predetermined threshold, the computer systemdetermining the refined tentative transcription of the sentence is thefinal refined tentative transcription; and the computer systempresenting the final refined tentative transcription as the accuratetranscription of the sentence.
 18. The computer system of claim 15,wherein the step of performing the syntactic, semantic, and redundancyanalyses of the variations includes the steps of: the computer systemgenerating first scores indicating measures of the syntaxes of thevariations satisfying grammar rules; the computer system generatingsecond scores indicating frequencies of fragments of the variationsbeing matched to fragments included in a corpus of documents; and thecomputer system generating third scores based on ratios of numbers ofdifferent words in sentences indicated by the variations and totalnumbers of words in the sentences indicated by the variations, whereinthe step of generating the plausibility scores for the variationsincludes generating each plausibility score by adding scores included inthe first, second and third scores.
 19. The computer system of claim 15,wherein the step of performing the syntactic, semantic, and redundancyanalyses of the variations includes the step of the computer systemperforming a redundancy analysis of a first variation included in thevariations by: determining a number d of different words in a sentenceindicated by the first variation; determining a total number t of wordsin the sentence indicated by the first variation; and determining aredundancy score of the first variation by calculating 100−100d/t. 20.The computer system of claim 19, wherein the method further comprisesthe steps of: the computer system determining a redundancy score of asecond variation included in the variations; the computer systemdetermining that the redundancy score of the first variation is greaterthan the redundancy score of the second variation; and based on theredundancy score of the first variation being greater than theredundancy score of the second variation, the computer systemdetermining that the first variation is more likely than the secondvariation to be the accurate transcription of the sentence.